Information visualization generally provides visual representations of digital data to reinforce human cognition. The data can have multiple attributes and include both numerical and non-numerical data types. The data can be displayed or visualized on a display device, such as a computer screen.
The data can be organized in different structures. For example, the data can be organized in a table structure using a model of vertical columns identifiable by name and horizontal rows, the cell being the unit where a row and column intersect. Each column in the table structure represents one data attribute. For example, a table with ten columns can represent ten attributes of data.
Also, the data can be organized in a hierarchical structure for the purposes of understanding data distributions by partitioning, where every element in the hierarchical structure, except one root element, is subordinate to a single other element. Such an element can be referred as a node, and nodes in the hierarchical structure form a parent-child relationship. The root element is usually referred as a root node. The root node does not have any parent, but has at least one child or multiple children.
One problem of the visualization of the digital data is rooted in the low-dimensionality of the display device. For example, the display device is usually two-dimensional, but the data to be visualized can have more attributes than two. This problem is even more apparent when the data are visualized to reveal comparative relationships within distributions of multiple attributes. It is easier for human eye to compare graphical objects of different positions, shapes, and/or sizes, than to compare the numbers. However, rendering graphical objects for multi-attribute data on the two-dimensional display in a manner suitable for visual comparison is challenging.
Graphical techniques for visual data comparison include bar charts, pie charts, and scatterplots, usually for one or perhaps two attributes. These graphical techniques are easy to use but offer limited information. For example, bar charts or pie charts show highly aggregated data while simultaneously omitting many other data values, such as data distribution of multiple attributes, patterns in data, correlations, or other detailed information.
The deficiency of the display device becomes even more apparent for visual comparison of hierarchical multi-attribute data. This is because the hierarchical structure provides a means of partitioning the data into meaningful units; it is desirable to preserve this hierarchical structure of the data on the display device. However, the values of the data visualized proportionally with different sizes and positions of graphical elements distort the layout of the structure making difficult the visual comparison of the hierarchical multi-attribute data.
For example, it is often required to compare values of structurally parallel data. For example, the user needs to compare the amount of different soft drinks specific people consume over a year. The data can be subdivided into different soft drinks and then in turn into measures of the amount each of a set of people consumed of those soft drinks in a year. The user would like to compare the amounts of soft drinks consumed by the same person. However, in some types of proportional visual presentations, the graphical values representing such comparable units of the data are sized with different aspect ratios and placed unpredictably on the display device. It can be difficult for the user to find and visually compare graphical objects located at semi-random positions on the display device since they are not aligned.
FIG. 1A shows a treemap visualization 110 of hierarchical multi-attribute data. Treemaps display hierarchical data as a set of nested rectangles, bringing out the overall proportional information within the hierarchical data structure. Each branch of the tree is given a rectangle, which is tiled with smaller rectangles representing sub-branches. The treemaps by construction make efficient use of space and illuminate proportional distributions of the data at high as well as low levels of the hierarchical structure. However, comparable values, including particularly leaf nodes, have rectangles that are placed in nonaligned positions, making them difficult to visually compare. For example, the rectangle 120 represents data comparable to rectangle 130. In this example, the area of rectangles 120 and 130 represent amounts of different soft drinks consumed by the same person. The hierarchy divides soft drinks by amount consumed per person, but, as can be seen from this example, the standard treemap visualization makes visual comparison of structurally comparable data difficult to find and compare.
FIG. 1B shows a generalized treemap framework that makes structurally comparable elements visually comparable by means of a matrix layout. The effect of a matrix layout is to make the graphical elements of parallel structures the same size and shape with the use of empty space. For example, in FIG. 1B, the values associated with Bridget 191 and Marty 192 are easy to compare. However, because intermediate parallel nodes are all of the same size, the visualization loses its power to present an easily understood comparative overview of the data at intermediate and higher levels of the hierarchy. For example, in FIG. 1B, an overall comparison of the number of cases of Family Law 160, Corporate Law 170, and Real Estate 180 is difficult to comprehend. To the extent the comparison can be comprehended, the individual cases at lower levels of the tree such as 191 and 192 have to be visually summed within each higher level structure and then mentally compared without the benefit of direct visual graphics.
Accordingly, there is a need in the art of digital data visualization to provide a system and a method for displaying hierarchical multi-attribute data for visual comparison of structurally parallel data.